Data Scenario and Model Hypothesis

Standard fit report for fits of SISCA to HG_Herring data.

Data Scenario: setup_HG

Model Hypothesis: setup_Multistock_AR1comps

Species:

Stocks:

Final phase convergence diagnostics

Max Gradient: 0.0067995

Objective Function value: 553.3575067

Time to fit model: 0.15

PD Hessian: FALSE

No. of Non-finite SEs: 2

Model fits

At-a-glance

Time series of spawning biomass with scaled spawn indices (top),
recruitments (second row), natural mortality (third row), and harvest rates (bottom row) for 
substocks of HG_Herring. Stocks are, from left to right,C/S, JP/S, Lou.

Figure 1: Time series of spawning biomass with scaled spawn indices (top), recruitments (second row), natural mortality (third row), and harvest rates (bottom row) for substocks of HG_Herring. Stocks are, from left to right,C/S, JP/S, Lou.

Fits to data

Model fits to spawn indices.

Figure 2: Model fits to spawn indices.

Average model fits to age data. Stocks are left to right, 
and gears are top to bottom.

Figure 3: Average model fits to age data. Stocks are left to right, and gears are top to bottom.

Model fits to age data, averaged over stock and time. Gears are top to bottom.

Figure 4: Model fits to age data, averaged over stock and time. Gears are top to bottom.

Table 1: Estimated standard deviations for observational data. The first three columns show age data sampling error standard deviations from the logistic-normal compositional likelihood function, and the last column shows spawn survey index standard deviations on the log scale.
\(\tau^{age}_{Red}\) \(\tau^{age}_{SR}\) \(\tau^{age}_{Gn}\) \(\tau^{surv}_{Su}\) \(\tau^{surv}_{D}\)
C/S 0.105 0.553 0.000 0.955 0.635
JP/S 0.503 0.478 0.632 0.512 0.498
Lou 0.000 0.467 0.000 0.831 0.813

Recruitment

Age-1 recruitments for all stocks. Equilibrium unfished recruitment $R_0$ is 
indicated by the horizontal dashed line. Second row shows recruitment residuals on the log scale, 
with the average of estimated residuals shown by the horizontal red dashed line.

Figure 5: Age-1 recruitments for all stocks. Equilibrium unfished recruitment \(R_0\) is indicated by the horizontal dashed line. Second row shows recruitment residuals on the log scale, with the average of estimated residuals shown by the horizontal red dashed line.

Stock-recruit curves (solid lines) and modeled recruitments (coloured points)

Figure 6: Stock-recruit curves (solid lines) and modeled recruitments (coloured points)

Selectivity and Catch

Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Figure 7: Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Figure 8: Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Selectivity-at-age for each fleet (rows). Aggregate stock average selectivity curves are shown as thick grey lines, while sub-stock specific estimates are shown as dashed thin coloured lines.

Figure 9: Selectivity-at-age for each fleet (rows). Aggregate stock average selectivity curves are shown as thick grey lines, while sub-stock specific estimates are shown as dashed thin coloured lines.

Reference Points

Yield Curves

Equilibrium yield curves as a function of fishing mortality rates, assuming all fishing mortality comes from the gillnet fleet.

Figure 10: Equilibrium yield curves as a function of fishing mortality rates, assuming all fishing mortality comes from the gillnet fleet.

Stock specific fits

C/S

Age composition fits

Model fits to yearly  C/S  stock age compositions for the  reduction  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 11: Model fits to yearly C/S stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to yearly  C/S  stock age compositions for the  seineRoe  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 12: Model fits to yearly C/S stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Age composition residuals for the  C/S sub-stock. Positive residuals are black  black, while negative residuals are red.

Figure 13: Age composition residuals for the C/S sub-stock. Positive residuals are black black, while negative residuals are red.

Age composition post tail compression

Model fits to tail compressed yearly  C/S  stock age compositions for the  reduction  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 14: Model fits to tail compressed yearly C/S stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to tail compressed yearly  C/S  stock age compositions for the  seineRoe  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 15: Model fits to tail compressed yearly C/S stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

JP/S

Age composition fits

Model fits to yearly  JP/S  stock age compositions for the  reduction  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 16: Model fits to yearly JP/S stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to yearly  JP/S  stock age compositions for the  seineRoe  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 17: Model fits to yearly JP/S stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to yearly  JP/S  stock age compositions for the  gillnet  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 18: Model fits to yearly JP/S stock age compositions for the gillnet fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Age composition residuals for the  JP/S sub-stock. Positive residuals are black  black, while negative residuals are red.

Figure 19: Age composition residuals for the JP/S sub-stock. Positive residuals are black black, while negative residuals are red.

Age composition post tail compression

Model fits to tail compressed yearly  JP/S  stock age compositions for the  reduction  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 20: Model fits to tail compressed yearly JP/S stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to tail compressed yearly  JP/S  stock age compositions for the  seineRoe  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 21: Model fits to tail compressed yearly JP/S stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to tail compressed yearly  JP/S  stock age compositions for the  gillnet  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 22: Model fits to tail compressed yearly JP/S stock age compositions for the gillnet fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Lou

Age composition fits

Model fits to yearly  Lou  stock age compositions for the  reduction  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 23: Model fits to yearly Lou stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Age composition residuals for the  Lou sub-stock. Positive residuals are black  black, while negative residuals are red.

Figure 24: Age composition residuals for the Lou sub-stock. Positive residuals are black black, while negative residuals are red.

Age composition post tail compression

Model fits to tail compressed yearly  Lou  stock age compositions for the  reduction  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 25: Model fits to tail compressed yearly Lou stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Optimisation performance

Objective function components

Table 2: Objective function components for data observations.
objFun obsSurface obsDive ageRed ageSR ageGill
Total 553.36 32.81 16.36 -10.05 -11.35 6.48
C/S 553.36 21.69 6.79 -11.81 8.52 0.00
JP/S 553.36 -4.78 -4.87 1.76 -15.74 6.48
Lou 553.36 15.89 14.44 0.00 -4.13 0.00
Table 3: Objective function components for standard (single level) and hyper-priors.
V1
objFun 553.360000
recDevs 234.870000
initDevs 28.600000
h 8.370000
M 0.120000
tvMdev 186.500000
IGtau_surf -2.320000
IGtau_dive -7.820000
tvSelAlpha 0.000000
tvSelBeta 0.000000
selAlphaRed 1.390000
selAlphaSR 1.390000
selAlphaGn -0.690000
selBetaRed -0.870000
selBetaSR -0.870000
selBetaGn -0.570000
lnB0 3.807282
lnRinit 16.111597
psiSOK -20.720000
Table 4: Objective function components for hierarchical (mult-level) priors.
V1
objFun 553.36
MDev 4.36
hDev 2.76
selAlphaDevR 4.15
selAlphaDevSR 3.40
selAlphaDevGn 2.76
selBetaDevR 3.15
selBetaDevSR 3.02
selBetaDevGn 2.76

Phase fit table

Table 5: Optimisation performance of SISCA for each phase.
phase objFun maxGrad nPar convCode convMsg time
1 1222.5151 0.0027357 9 0 relative convergence (4) 0.0172167
2 1220.6504 0.0010026 19 0 relative convergence (4) 0.0097500
3 1135.4440 0.0001822 87 0 relative convergence (4) 0.0109833
4 807.8843 0.0004111 255 0 relative convergence (4) 0.0152000
5 803.3253 0.0001799 258 0 relative convergence (4) 0.0139667
6 798.0121 0.0007829 262 0 relative convergence (4) 0.0157333
7 793.5877 0.0010861 265 0 relative convergence (4) 0.0159333
8 557.8597 0.0012759 272 0 relative convergence (4) 0.0246333
9 553.3575 0.0067995 277 0 relative convergence (4) 0.0228333
RE NA NA NA NA NA NA

Leading Parameter SDReport

Table 6: SD report showing leading parameter estimates, standard errors, gradient components, and coefficients of variation. Gradients with a magnitude above 1e-3 are shown in bold red, while the coefficients of variation (cv) are
coloured so that smaller values are lighter in colour, and larger values are darker, with cvs above .5 in bold, and cvs above 3 in red.
est se gr cv
lnB0_p 0.9572 0.0744 0.001 0.0777
lnB0_p.1 2.7486 0.1430 0.0097 0.052
lnB0_p.2 0.1015 0.1797 0.00093 1.771
lnRinit_p 5.9871 0.1863 0.00038 0.0311
lnRinit_p.1 5.6258 0.4143 0.0012 0.0736
lnRinit_p.2 4.4987 0.4418 0.0023 0.0982
logit_ySteepness -0.1041 0.4396 -0.00048 4.2212
lnM -0.9908 0.1363 -0.0051 0.1376
fDevs_ap -0.0258 0.5265 1e-04 20.3997
fDevs_ap.1 -0.0424 0.5196 0.00016 12.2673
fDevs_ap.2 0.4102 0.4972 0.00025 1.212
fDevs_ap.3 1.1641 0.4776 0.00028 0.4102
fDevs_ap.4 0.0822 0.5308 8.4e-05 6.4541
fDevs_ap.5 0.4096 0.5359 6.3e-05 1.3084
fDevs_ap.6 -0.4515 0.8085 2e-05 1.7905
fDevs_ap.7 -0.2851 0.8764 1.3e-05 3.0741
fDevs_ap.8 -0.1814 0.9223 7.1e-06 5.0851
fDevs_ap.9 -0.2200 0.9060 1.2e-05 4.1186
epsM_p 1.3349 0.6084 -9.8e-05 0.4558
epsM_p.1 -0.4868 0.6139 5e-04 1.2612
epsM_p.2 -1.0885 0.6873 -0.0014 0.6314
lnSelAlpha_g 1.0726 0.0549 -0.0021 0.0512
lnSelAlpha_g.1 1.3217 0.1071 0.0016 0.081
lnSelAlpha_g.2 1.6506 0.0404 1.2e-05 0.0245
lnSelBeta_g 0.5917 0.0994 -0.00032 0.168
lnSelBeta_g.1 0.5855 0.1197 -5.4e-05 0.2044
lnSelBeta_g.2 0.3091 0.1112 0.0032 0.3597
epsSelAlpha_pg -1.6709 0.4117 2.2e-05 0.2464
epsSelAlpha_pg.1 0.6641 0.3767 -8.5e-05 0.5673
epsSelAlpha_pg.2 -0.0428 0.3665 0.00051 8.567
epsSelAlpha_pg.3 -0.9195 0.3869 3.6e-05 0.4207
epsSelBeta_pg -0.8904 0.7730 8.6e-06 0.8682
epsSelBeta_pg.1 0.4928 0.4353 -2.3e-05 0.8832
epsSelBeta_pg.2 -0.4657 0.4268 8.4e-07 0.9163
epsSelBeta_pg.3 -0.2579 0.6106 5.6e-06 2.3672
lntau2Obs_pg -0.0921 0.2239 3.3e-05 2.4295
lntau2Obs_pg.1 -1.3394 0.1942 -3.6e-05 0.145
lntau2Obs_pg.2 -0.3700 0.2099 0.00016 0.5673
lntau2Obs_pg.3 -0.9079 0.2040 -6.3e-05 0.2246
lntau2Obs_pg.4 -1.3947 0.2019 -5.1e-05 0.1448
lntau2Obs_pg.5 -0.4152 0.2772 2e-05 0.6676
recDevs_vec 0.0501 0.1165 0.00012 2.3255
recDevs_vec.1 -0.2546 NaN 0.00013 NaN
recDevs_vec.2 1.4775 NaN 0.00033 NaN
recDevs_vec.3 -0.8266 0.7712 1.4e-05 0.933
recDevs_vec.4 -0.5716 0.8460 8.9e-06 1.4799
recDevs_vec.5 -0.9652 0.7782 9.5e-06 0.8063
recDevs_vec.6 -0.9350 0.8020 1.4e-06 0.8578
recDevs_vec.7 -0.2545 0.9360 4.5e-06 3.6784
recDevs_vec.8 2.0105 0.3649 6.4e-05 0.1815
recDevs_vec.9 0.0608 1.0259 1.1e-05 16.8848
recDevs_vec.10 -0.1326 0.5694 1.8e-05 4.2956
recDevs_vec.11 0.2093 0.6456 2.8e-05 3.0853
recDevs_vec.12 0.5573 0.6134 3.4e-06 1.1005
recDevs_vec.13 1.0475 0.4509 6.5e-05 0.4304
recDevs_vec.14 1.4803 0.3002 0.00015 0.2028
recDevs_vec.15 0.9403 0.2693 -0.00012 0.2864
recDevs_vec.16 1.4228 0.2790 0.00016 0.1961
recDevs_vec.17 -0.3716 0.3745 3.3e-05 1.0076
recDevs_vec.18 0.5851 0.3779 3.3e-05 0.6458
recDevs_vec.19 -0.9582 0.4573 6.4e-06 0.4772
recDevs_vec.20 -0.6933 0.7705 1.2e-05 1.1113
recDevs_vec.21 -1.0024 0.7172 1.1e-05 0.7154
recDevs_vec.22 -0.5095 0.4372 -1.3e-05 0.8582
recDevs_vec.23 2.7691 0.3266 7.3e-05 0.118
recDevs_vec.24 0.3898 0.3327 -0.00013 0.8536
recDevs_vec.25 -0.0269 0.3788 1.7e-05 14.0681
recDevs_vec.26 0.1179 0.3811 1.1e-06 3.2324
recDevs_vec.27 1.2796 0.3764 1.3e-06 0.2942
recDevs_vec.28 0.6184 0.3866 1.4e-05 0.6251
recDevs_vec.29 0.2378 0.3863 8.5e-07 1.6244
recDevs_vec.30 0.0802 0.3875 -2.2e-05 4.8341
recDevs_vec.31 0.7441 0.3384 -1.7e-05 0.4548
recDevs_vec.32 1.1033 0.3044 1.8e-05 0.2759
recDevs_vec.33 0.2136 0.3236 1.3e-05 1.5148
recDevs_vec.34 -0.6561 0.3341 -2.1e-05 0.5092
recDevs_vec.35 0.7661 0.2995 7.1e-05 0.391
recDevs_vec.36 -1.3997 0.3411 -1.9e-05 0.2437
recDevs_vec.37 -1.6490 0.3460 6.8e-06 0.2098
recDevs_vec.38 -1.2834 0.3434 -1.1e-05 0.2676
recDevs_vec.39 0.0561 0.3641 9.6e-06 6.4854
recDevs_vec.40 -0.3326 0.4227 -9.6e-06 1.2709
recDevs_vec.41 -0.0901 0.5701 -6e-06 6.3304
recDevs_vec.42 -0.3814 0.6392 -2.6e-06 1.6759
recDevs_vec.43 0.2631 0.5757 -8e-06 2.1883
recDevs_vec.44 0.8676 0.5423 -1.9e-05 0.625
recDevs_vec.45 0.0016 0.6670 -1.2e-05 408.9609
recDevs_vec.46 0.2849 0.9697 -1.2e-05 3.4043
recDevs_vec.47 0.8125 0.8874 -2.1e-05 1.0921
recDevs_vec.48 0.1694 1.0125 -9.1e-06 5.9763
recDevs_vec.49 0.5011 0.7444 -1.3e-05 1.4855
recDevs_vec.50 0.9800 0.5476 -2.2e-05 0.5588
recDevs_vec.51 -0.4168 0.6263 -3.2e-07 1.5028
recDevs_vec.52 1.1060 0.5211 -2.9e-05 0.4711
recDevs_vec.53 -0.6467 0.6714 -7.5e-06 1.0381
recDevs_vec.54 -0.5483 0.7631 -1e-05 1.3917
recDevs_vec.55 0.6401 0.8292 -1.9e-05 1.2955
recDevs_vec.56 -0.1089 0.8871 -7.6e-06 8.1434
recDevs_vec.57 -0.3507 0.8350 -4.8e-06 2.3813
recDevs_vec.58 -0.3394 0.8109 -4.3e-06 2.3893
recDevs_vec.59 2.7180 0.2263 0.0021 0.0833
recDevs_vec.60 0.3724 0.3767 0.00038 1.0115
recDevs_vec.61 -0.1038 0.3917 0.00029 3.774
recDevs_vec.62 -0.3450 0.3804 0.00027 1.1024
recDevs_vec.63 -0.2050 0.4433 0.00024 2.1619
recDevs_vec.64 0.4872 0.5857 0.00038 1.2022
recDevs_vec.65 0.1618 0.6806 0.00024 4.2053
recDevs_vec.66 0.9482 0.4761 0.00052 0.5021
recDevs_vec.67 1.0595 0.4726 0.00067 0.446
recDevs_vec.68 0.9732 0.4967 0.00079 0.5104
recDevs_vec.69 0.4595 0.6357 0.00064 1.3834
recDevs_vec.70 0.8957 0.5195 0.00084 0.58
recDevs_vec.71 -0.9555 0.7178 7e-05 0.7512
recDevs_vec.72 -1.4819 0.6697 1.9e-05 0.4519
recDevs_vec.73 -1.3419 0.6765 2e-05 0.5041
recDevs_vec.74 -1.2696 0.6835 2e-05 0.5384
recDevs_vec.75 -1.0541 0.4847 5.1e-05 0.4598
recDevs_vec.76 -0.3018 0.4087 7.8e-05 1.3542
recDevs_vec.77 -0.5792 0.3711 3.9e-05 0.6407
recDevs_vec.78 0.1919 0.2765 0.00011 1.4413
recDevs_vec.79 1.2101 0.2478 6.2e-05 0.2048
recDevs_vec.80 1.5273 0.2344 -0.00018 0.1535
recDevs_vec.81 0.0130 0.2898 0.0023 22.2687
recDevs_vec.82 0.1385 0.3001 -0.00051 2.1664
recDevs_vec.83 0.3955 0.2905 0.00047 0.7345
recDevs_vec.84 -0.1195 0.2940 -0.00089 2.4596
recDevs_vec.85 2.8959 0.2448 0.0026 0.0845
recDevs_vec.86 0.7132 0.2499 -0.00024 0.3504
recDevs_vec.87 -0.7251 0.2637 0.00032 0.3637
recDevs_vec.88 -0.9766 0.2696 -8.6e-05 0.2761
recDevs_vec.89 1.2297 0.2513 -0.00032 0.2044
recDevs_vec.90 0.4481 0.2498 -3e-04 0.5575
recDevs_vec.91 -1.4444 0.2728 -0.00046 0.1889
recDevs_vec.92 -1.2138 0.2727 -0.00086 0.2246
recDevs_vec.93 0.9289 0.2428 -0.00045 0.2614
recDevs_vec.94 0.3838 0.2420 0.00037 0.6305
recDevs_vec.95 -0.9451 0.2694 0.00024 0.285
recDevs_vec.96 -1.5536 0.2888 -1.6e-05 0.1859
recDevs_vec.97 0.8090 0.2575 0.00023 0.3183
recDevs_vec.98 -1.9434 0.3902 -1.8e-05 0.2008
recDevs_vec.99 -1.6609 0.3912 -0.00024 0.2356
recDevs_vec.100 -0.6092 0.3666 -0.00041 0.6017
recDevs_vec.101 0.4887 0.2770 2.3e-05 0.5668
recDevs_vec.102 0.7780 0.2649 2e-04 0.3405
recDevs_vec.103 1.9303 0.2288 5e-04 0.1185
recDevs_vec.104 -1.5340 0.2869 -0.00017 0.187
recDevs_vec.105 -0.4348 0.2859 2e-04 0.6576
recDevs_vec.106 -0.4086 0.2993 -0.00024 0.7326
recDevs_vec.107 -0.5068 0.2940 4.8e-05 0.5801
recDevs_vec.108 1.0522 0.2789 6.1e-05 0.265
recDevs_vec.109 -0.7096 0.3869 -3.8e-05 0.5453
recDevs_vec.110 0.7217 0.3603 1.7e-05 0.4991
recDevs_vec.111 -0.6499 0.4289 2.3e-05 0.6599
recDevs_vec.112 1.0308 0.3628 6.5e-06 0.352
recDevs_vec.113 -0.6852 0.3715 1.4e-05 0.5421
recDevs_vec.114 1.2196 0.3233 -1.9e-05 0.2651
recDevs_vec.115 -1.0631 0.4152 2.4e-05 0.3906
recDevs_vec.116 0.5850 0.3561 -8.5e-06 0.6087
recDevs_vec.117 -0.1199 0.3815 1.5e-05 3.1808
recDevs_vec.118 1.9730 0.3098 1.7e-06 0.157
recDevs_vec.119 -0.2115 0.3717 -1.3e-05 1.7569
recDevs_vec.120 0.1940 0.3526 -5.7e-06 1.8172
recDevs_vec.121 1.9651 0.3163 -1.6e-05 0.161
recDevs_vec.122 2.7422 0.2583 6.1e-05 0.0942
recDevs_vec.123 1.9988 0.2783 2.1e-05 0.1392
recDevs_vec.124 0.6360 0.3100 2.8e-05 0.4875
recDevs_vec.125 -0.4425 0.3271 7.5e-05 0.7393
recDevs_vec.126 0.1432 0.3732 -3.5e-05 2.6057
recDevs_vec.127 0.3430 0.4028 -7.5e-06 1.1744
recDevs_vec.128 -0.6856 0.4312 -1.3e-05 0.629
recDevs_vec.129 2.0590 0.3838 -4.9e-05 0.1864
recDevs_vec.130 -0.2196 0.5580 3.2e-05 2.5412
recDevs_vec.131 -0.6246 0.6232 -7.6e-06 0.9979
recDevs_vec.132 -0.4944 0.6845 1.3e-05 1.3845
recDevs_vec.133 -0.0105 0.9515 -4e-07 90.5449
recDevs_vec.134 -0.0519 0.9300 6.2e-07 17.9045
recDevs_vec.135 -0.1472 0.8811 6.1e-06 5.9847
recDevs_vec.136 -0.1951 0.7667 1.4e-05 3.9294
recDevs_vec.137 3.9558 0.2364 -0.00012 0.0598
recDevs_vec.138 0.4863 0.2789 -9.2e-05 0.5735
recDevs_vec.139 0.6746 0.3140 -3.5e-05 0.4655
recDevs_vec.140 -1.0979 0.3810 -7.4e-06 0.347
recDevs_vec.141 0.3465 0.3987 7.8e-07 1.1507
recDevs_vec.142 -1.5687 0.6933 -2.5e-06 0.442
recDevs_vec.143 -1.8212 0.6805 -5.2e-08 0.3737
recDevs_vec.144 -1.5312 0.6976 -1.5e-06 0.4556
recDevs_vec.145 -1.3454 0.6365 -7.8e-06 0.4731
recDevs_vec.146 -0.2126 0.5886 2.9e-06 2.7688
recDevs_vec.147 -0.8540 0.7844 -4e-06 0.9185
recDevs_vec.148 -0.8072 0.7893 -3.4e-06 0.9778
recDevs_vec.149 -0.6322 0.8003 -1.4e-06 1.2661
recDevs_vec.150 -0.7059 0.7816 -2.6e-06 1.1073
recDevs_vec.151 0.2042 0.5643 -1e-05 2.7635
recDevs_vec.152 0.0780 0.8668 4.4e-06 11.118
recDevs_vec.153 0.2571 0.9575 3.4e-06 3.7245
recDevs_vec.154 -0.1067 0.9073 -1.1e-06 8.5016
recDevs_vec.155 -0.3484 0.8836 -1e-06 2.5364
recDevs_vec.156 -0.4624 0.8540 -1.3e-06 1.847
recDevs_vec.157 -0.4103 0.8627 -7.4e-07 2.1028
recDevs_vec.158 -0.2978 0.8818 -7.9e-07 2.9614
recDevs_vec.159 -0.3257 0.8663 -9.7e-07 2.6598
recDevs_vec.160 -0.3195 0.8587 -6.9e-07 2.688
recDevs_vec.161 -0.1355 0.9131 -2.1e-07 6.7371
recDevs_vec.162 0.3278 0.9868 3e-07 3.0099
recDevs_vec.163 0.0960 1.0386 4.2e-07 10.8191
recDevs_vec.164 -0.0816 0.9729 1.8e-08 11.9217
omegaM_pt -0.0900 0.8227 -0.00049 9.1427
omegaM_pt.1 -0.1133 0.6497 -0.00048 5.7349
omegaM_pt.2 0.0737 0.6449 -0.00039 8.7482
omegaM_pt.3 0.1573 0.6540 -0.00028 4.1573
omegaM_pt.4 0.2845 0.6698 -0.00016 2.3546
omegaM_pt.5 0.4587 0.6658 -3.3e-05 1.4517
omegaM_pt.6 0.3960 0.6305 1.6e-05 1.5919
omegaM_pt.7 0.3975 0.6608 6e-05 1.6625
omegaM_pt.8 0.2889 0.5540 0.00012 1.9178
omegaM_pt.9 0.2315 0.5565 0.00024 2.4038
omegaM_pt.10 0.1711 0.5580 0.00039 3.261
omegaM_pt.11 0.0958 0.5583 0.00059 5.8266
omegaM_pt.12 0.0905 0.5577 0.00082 6.1637
omegaM_pt.13 0.2055 0.5562 0.0011 2.7072
omegaM_pt.14 0.2290 0.5536 0.0012 2.4179
omegaM_pt.15 0.0163 0.5564 0.0012 34.0351
omegaM_pt.16 -0.2135 0.5585 0.0011 2.6156
omegaM_pt.17 -0.3138 0.5587 0.0011 1.7806
omegaM_pt.18 -0.3092 0.5576 0.0011 1.803
omegaM_pt.19 -0.2470 0.5552 0.0011 2.2477
omegaM_pt.20 -0.1828 0.5534 0.0011 3.0271
omegaM_pt.21 -0.1386 0.5513 0.0011 3.9777
omegaM_pt.22 -0.0531 0.5491 0.0011 10.3427
omegaM_pt.23 0.1017 0.5483 0.0011 5.3928
omegaM_pt.24 0.2254 0.5483 0.0012 2.4325
omegaM_pt.25 0.3167 0.5480 0.0013 1.7304
omegaM_pt.26 0.3582 0.5475 0.0013 1.5284
omegaM_pt.27 0.2877 0.5472 0.0014 1.9018
omegaM_pt.28 0.2722 0.5477 0.0015 2.0122
omegaM_pt.29 0.2735 0.5480 0.0016 2.0039
omegaM_pt.30 0.2582 0.5479 0.0017 2.122
omegaM_pt.31 0.2469 0.5479 0.0018 2.2195
omegaM_pt.32 0.2692 0.5474 0.0019 2.0332
omegaM_pt.33 0.2693 0.5470 0.0019 2.0313
omegaM_pt.34 0.1757 0.5466 0.0019 3.1104
omegaM_pt.35 -0.0072 0.5465 0.0019 75.942
omegaM_pt.36 -0.0338 0.5447 0.0018 16.0928
omegaM_pt.37 -0.0143 0.5425 0.0017 37.9281
omegaM_pt.38 0.1854 0.5403 0.0016 2.9143
omegaM_pt.39 0.3852 0.5391 0.0015 1.3995
omegaM_pt.40 0.5499 0.5403 0.0015 0.9824
omegaM_pt.41 0.6523 0.5423 0.0015 0.8314
omegaM_pt.42 0.5732 0.5420 0.0014 0.9455
omegaM_pt.43 0.5002 0.5405 0.0013 1.0805
omegaM_pt.44 0.2479 0.5431 0.0012 2.1911
omegaM_pt.45 0.1972 0.5434 0.0012 2.7549
omegaM_pt.46 0.3122 0.5420 0.0011 1.7362
omegaM_pt.47 0.4390 0.5414 0.001 1.2332
omegaM_pt.48 0.3699 0.5398 0.001 1.4595
omegaM_pt.49 0.3352 0.5411 0.00095 1.6141
omegaM_pt.50 0.2494 0.5440 0.00088 2.1812
omegaM_pt.51 -0.0118 0.5431 0.00083 46.0907
omegaM_pt.52 -0.0663 0.5445 0.00077 8.2109
omegaM_pt.53 -0.1377 0.5439 0.00072 3.9495
omegaM_pt.54 -0.2670 0.5441 0.00067 2.0377
omegaM_pt.55 -0.2257 0.5441 0.00062 2.4108
omegaM_pt.56 -0.2203 0.5436 0.00058 2.4677
omegaM_pt.57 -0.1264 0.5438 0.00053 4.3019
omegaM_pt.58 -0.0187 0.5432 0.00048 29.0724
omegaM_pt.59 0.0732 0.5413 0.00043 7.3898
omegaM_pt.60 0.1419 0.5413 0.00039 3.8145
omegaM_pt.61 0.2511 0.5425 0.00035 2.16
omegaM_pt.62 0.3497 0.5443 3e-04 1.5567
omegaM_pt.63 0.4555 0.5441 0.00025 1.1945
omegaM_pt.64 0.3121 0.5495 2e-04 1.7606
omegaM_pt.65 -0.0092 0.5556 0.00015 60.1999
omegaM_pt.66 -0.2151 0.5633 1e-04 2.6194
omegaM_pt.67 -0.1077 0.5704 5.1e-05 5.297
logitphi1_g 0.0942 0.5687 -1.4e-06 6.0378
logitphi1_g.1 0.7632 0.2069 -3.8e-05 0.2711
logitphi1_g.2 0.6278 0.4508 -0.0055 0.7181

MCMC posteriors

MCMC performance

## Not Yet Implemented

Other

Compositional Likelihood Correlation Matrices

Estimated correlation matrices for age composition residuals in the  reduction  fleet. The circles above the visualise the numbers below the diagonal.

Figure 26: Estimated correlation matrices for age composition residuals in the reduction fleet. The circles above the visualise the numbers below the diagonal.

Estimated correlation matrices for age composition residuals in the  seineRoe  fleet. The circles above the visualise the numbers below the diagonal.

Figure 27: Estimated correlation matrices for age composition residuals in the seineRoe fleet. The circles above the visualise the numbers below the diagonal.

Estimated correlation matrices for age composition residuals in the  gillnet  fleet. The circles above the visualise the numbers below the diagonal.

Figure 28: Estimated correlation matrices for age composition residuals in the gillnet fleet. The circles above the visualise the numbers below the diagonal.

Compositional Likelihood Diagnostic Plot

Diagnostic plot for compositional likelihood function.

Figure 29: Diagnostic plot for compositional likelihood function.

Comparisons with ISCAM

Plots of average age composition fits at the major stock level. Left is SISCA, right is ISCAM.

Figure 30: Plots of average age composition fits at the major stock level. Left is SISCA, right is ISCAM.

Comparison of spawning stock biomass and age-2 recruitment at the major stock level between ISCAM and SISCA.

Figure 31: Comparison of spawning stock biomass and age-2 recruitment at the major stock level between ISCAM and SISCA.